sybil (version 1.0.2)

fluxVar: Flux Variability Analysis

Description

Performs flux variablilty analysis for a given model.

Usage

fluxVar(model, react, percentage = 100,
          tol = SYBIL_SETTINGS("TOLERANCE"),
          lpdir = SYBIL_SETTINGS("OPT_DIRECTION"),
          solver = SYBIL_SETTINGS("SOLVER"),
          method = SYBIL_SETTINGS("METHOD"),
          solverParm = SYBIL_SETTINGS("SOLVER_CTRL_PARM"),
          fld = FALSE, verboseMode = 2, ...)

Arguments

model

An object of class modelorg.

react

An object of class reactId, character or integer. Specifies the fluxes (variables) to analyse. If left empty: react_id(model).

percentage

Consider solutions with x percent of the optimal solution. Default: 100

tol

Tolerance value. Default: SYBIL_SETTINGS("TOLERANCE").

lpdir

Character value, direction of optimisation. Can be set to "min" or "max". Default: SYBIL_SETTINGS("OPT_DIRECTION").

solver

Single character value. The solver to use. See SYBIL_SETTINGS for possible values. Default: SYBIL_SETTINGS("SOLVER").

method

Single character value. The optimization algorithm to use. Possible values depend on the setting in solver. See SYBIL_SETTINGS for possible values. Default: SYBIL_SETTIN

solverParm

A data frame containing parameters for the specified solver. Default: SYBIL_SETTINGS("SOLVER_CTRL_PARM").

fld

Boolean. Save the resulting flux distribution. Default: FALSE

verboseMode

An integer value indicating the amount of output to stdout: 0: nothing, 1: status messages, 2: like 1 plus a progress indicator, 3: a table containing the reaction id's and the corresponding min max values. Default: 2.

...

Further arguments passed to simpleFBA.

Value

• An object of class optsol_fluxVar.

encoding

utf8

Details

The function fluxVar performs a flux variability analysis with a given model. The minimum and maximum flux values for each reaction in the model are calculated, which still support a given optimal functional state $Z_{\mathrm{opt}}$. For each flux $i$ two linear programming problems are solved $$\begin{array}{rll} \max \textrm{ or } \min & v_i \[1ex] \mathrm{s.\,t.} & Z = Z_{\mathrm{opt}} \[1ex] & \mbox{\boldmath$Sv$\unboldmath} = 0 \[1ex] & \alpha_i \leq v_i \leq \beta_i & \quad \forall i \in {1, \ldots, n} \[1ex] \end{array}$$ with $\bold{S}$ beeing the stoichiometric matrix, $\alpha_i$ and $\beta_i$ beeing the lower and upper bounds for flux (variable) $i$. The total number of variables of the optimization problem is denoted by $n$. The result of the optimization is returned as object of class optsol_fluxVar containing the range of each flux still supporting the given optimal state. The optimal state $Z_{\mathrm{opt}}$ is calculated via flux balance analysis (see also simpleFBA). The objective function here is the one given in the model.

References

Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. Ø. and Herrgard, M. J. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2, 727--738.

Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., Kang, J., Hyduke, D. R. and Palsson, B. Ø. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6, 1290--1307.

Bernhard Ø. Palsson (2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press.

Examples

data(Ec_core)
  fv <- fluxVar(Ec_core)
  plot(fv)